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Connect Google Merchant Center to PostgreSQL

Connect Google Merchant Center to PostgreSQL and export selected accounts, metrics, and dimensions to your PostgreSQL project.

Configure the historical window, destination table, and recurring schedule without maintaining a Google Merchant Center API pipeline.

Used by the world's leading companies

Export Google Merchant Center data to PostgreSQL

Catchr handles extraction, normalization, and loading automatically.

Centralize

Centralize Google Merchant Center data in PostgreSQL

Bring all your Google Merchant Center data into your warehouse and make it available for analytics, machine learning, and internal applications.

Data freshness

Keep your PostgreSQL tables up to date

Automatically sync new data from Google Merchant Center so every dashboard, model, and report works with fresh information.

Analysis

Unify Google Merchant Center with the rest of your business data

Join marketing, finance, CRM, and product data inside the same warehouse.

Google Merchant Center metrics and dimensions available in PostgreSQL

Access 28 metrics and 90 dimensions from Google Merchant Center connectors including Clicks, Impressions, CTR, Conversions, Conversion Value, Conversion Rate, Date, Product Title, Product ID, Product Brand Name, Product Category L1, Product Channel, Product Destination Status, Product Availability and many more...

28

Metrics availables

90

Dimensions availables

Connect Google Merchant Center to PostgreSQL in minutes

Move your data from Google Merchant Center to PostgreSQL with a simple setup. Once connected, Catchr handles the ingestion automatically so your warehouse stays up to date without manual work.

Step one

Connect your Google Merchant Center account

Authenticate your Google Merchant Center account securely in Catchr. No custom scripts, API maintenance, or engineering work required.

Client A · Connected sources
5 sources ready
Google Merchant Center 3 advertising accounts Connected
Google Ads 2 advertising accounts Connected
Google Analytics 4 1 web property Connected
HubSpot 1 CRM portal Connected
Step two

Choose the PostgreSQL destination

Add the database host and credentials once. Catchr checks the connection so your exports start with a reachable destination.

Destination / PostgreSQL Required fields
Host Required
warehouse.company.net
Username Required
catchr_writer
Password Required
••••••••••••
Database Required
marketing
Step three

Keep your PostgreSQL tables updated

Choose the fields, initial history, partition field, and recurring schedule for the job. Catchr refreshes the configured import window in your PostgreSQL table.

Export job / PostgreSQL Example
Schema Destination
marketing_raw
Table Per job
linkedin_ads_daily
Recurring schedule Choose a cadence
Every 6 hours
Daily · 07:00
Weekly · Mon
First sync scheduledSelected fields · configured history
07:00 UTC

Review 30-day Google Merchant Center product visibility in SQL.

This example summarizes product impressions, clicks, conversions, and click-through rate.

Google Merchant Center.sql
BigQuery SQL
30-day window
SELECT
  product_id,
  SUM(impressions) AS impressions,
  SUM(clicks) AS clicks,
  SUM(conversions) AS conversions,
  SAFE_DIVIDE(
    SUM(clicks),
    SUM(impressions)
  ) AS ctr
FROM `your_project.marketing_raw.google_merchant_center_products_daily`
WHERE date >= DATE_SUB(
  CURRENT_DATE(),
  INTERVAL 30 DAY
)
GROUP BY product_id
ORDER BY clicks DESC;
Illustrative SQL — adapt it to the fields and schema selected in your export. Standard SQL

Choose what to load into PostgreSQL.

Each connector has its own entities, fields, historical limits, and reporting uses. Open a source page for the details that belong to that platform.

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Before you schedule the job.

Answers about PostgreSQL setup, available source data, schedules, table updates, and SQL use cases.

Which steps connect Google Merchant Center with a PostgreSQL destination?

Register Google Merchant Center, save the PostgreSQL connection details, and pair both systems in a datastream. The associated job previews and writes the selected columns to "accounts".

What schema can I build from Google Merchant Center data in PostgreSQL?

Select concrete Google Merchant Center fields such as Average Orders Sales, Average Orders Value, Clicks, and Account Status - Account Level Issue Country. You can rename the chosen columns, preview the result, and write them to PostgreSQL tables such as accounts, products, and product groups.

How are Google Merchant Center history and recurring PostgreSQL loads scheduled?

The "accounts" job can begin with a historical Google Merchant Center fetch and continue on a recurring schedule. Available history and frequency depend on the source API and your plan.

How does Catchr update existing Google Merchant Center records in PostgreSQL?

Select a valid date field when configuring the Google Merchant Center job for "accounts". Catchr deletes and rewrites the scheduled window, limiting duplicate rows across overlapping runs.

Which dashboards can use Google Merchant Center data stored in PostgreSQL?

With Google Merchant Center data, a PostgreSQL query can summarize product impressions, clicks, conversions, and click-through rate. Turn Average Orders Sales and Average Orders Value into a stable PostgreSQL view that analysts and reporting tools can query repeatedly.

Does my PostgreSQL database need to exist before I connect it?

Yes. Connect an existing PostgreSQL database first. The Catchr job creates and populates the destination table you configure inside that database.

Can I load several marketing sources into the same Postgres database?

Yes. Create a separate datastream and job for each source, write each export to its own table, then combine the data downstream with SQL, views, or your BI tool.

Still have a question ? 

Our teams is always here to responds to any question you could have about our data connector. 

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